AI Agents Are Failing and It's Almost Never the Model's Fault | Alberto Pan, Denodo

Enterprise adoption of generative and agentic AI remains sluggish primarily due to a "trust gap" stemming from fundamental data architecture failures. Organizations often struggle with AI hallucinations and inconsistent decision-making because models lack access to real-time, contextually accurate data across fragmented sources. Traditional data warehouses and lakehouses, designed for analytics, introduce latency through physical replication and fail to provide the unified semantic layer necessary for operational AI. Denodo addresses these bottlenecks by implementing a logical data management approach, enabling real-time access to data where it lives without requiring upfront consolidation. This architecture enforces consistent security policies and business definitions, which are critical as enterprises transition from isolated analytical pilots to multi-agent workflows. By moving beyond ad hoc, siloed data layers, organizations can establish the reliable, governed foundation required to scale agentic AI in production environments.